In this paper, we study the GJR scaling model which embeds the intraday return processes into the daily GJR model and propose a class of robust M-estimates for it. The estimation procedures would be more efficient whe...In this paper, we study the GJR scaling model which embeds the intraday return processes into the daily GJR model and propose a class of robust M-estimates for it. The estimation procedures would be more efficient when high-frequency data is taken into the model. However, high-frequency data brings noises and outliers which may lead to big bias of the estimators. Therefore, robust estimates should be taken into consideration. Asymptotic results are derived from the robust M-estimates without the finite fourth moment of the innovations. A simulation study is carried out to assess the performance of the model and its estimates.Robust M-estimate of GJR model is also applied in predicting Va R for real financial time series.展开更多
基于不需要后验密度解析形式的随机梯度哈密尔顿蒙特卡洛(stochastic gradient Hamiltonian Monte Carlo,SGHMC)方法对AR-GJR-GARCH模型的参数进行了贝叶斯估计。以2019.3.13—2020.1.2和2020.1.3—2020.11.3两个时间段的中证医药指数...基于不需要后验密度解析形式的随机梯度哈密尔顿蒙特卡洛(stochastic gradient Hamiltonian Monte Carlo,SGHMC)方法对AR-GJR-GARCH模型的参数进行了贝叶斯估计。以2019.3.13—2020.1.2和2020.1.3—2020.11.3两个时间段的中证医药指数的数据为例,对本文提出的方法进行了检验。结果显示,所得的参数估计值反映了与该指数的波动性相关的市场背景信息。展开更多
基金Supported by National Natural Science Foundation of China(Grant No.71003100)the Research Funds of Renmin University of China(No.11XNK027)
文摘In this paper, we study the GJR scaling model which embeds the intraday return processes into the daily GJR model and propose a class of robust M-estimates for it. The estimation procedures would be more efficient when high-frequency data is taken into the model. However, high-frequency data brings noises and outliers which may lead to big bias of the estimators. Therefore, robust estimates should be taken into consideration. Asymptotic results are derived from the robust M-estimates without the finite fourth moment of the innovations. A simulation study is carried out to assess the performance of the model and its estimates.Robust M-estimate of GJR model is also applied in predicting Va R for real financial time series.
文摘基于不需要后验密度解析形式的随机梯度哈密尔顿蒙特卡洛(stochastic gradient Hamiltonian Monte Carlo,SGHMC)方法对AR-GJR-GARCH模型的参数进行了贝叶斯估计。以2019.3.13—2020.1.2和2020.1.3—2020.11.3两个时间段的中证医药指数的数据为例,对本文提出的方法进行了检验。结果显示,所得的参数估计值反映了与该指数的波动性相关的市场背景信息。
